BATTERY DEGRADATION ADDITION TO SANDIA LABS QUEST 2.0 SOFTWARE

Abstract

Advanced energy storage systems (ESS) are increasingly being used to solve problems in power generation systems and meet the ever-changing demands of the electric grid. As the power grid continues to diversify, the role of ESS in providing stability, peak shaving, and load shifting is becoming increasingly more essential. When considering the installation of an ESS, a technoeconomic analysis is challenging if accurate degradation modeling is not used, which is critical for understanding long-term performance and cost. Degradation in batteries is caused by complex chemical mechanisms and often occurs nonlinearly, making simple predictive models unreliable. This can lead to underestimated operational costs and reduced reliability when ESS underperform in the field. To improve this analysis, Sandia National Laboratories’ QuESt 2.0 tool, which assists in ESS technoeconomic evaluations, is being expanded to include degradation modeling. This study aims to make including a degradation model in this analysis easier by using publicly available degradation data from BatteryArchive.org to build models for use in QuESt applications. The archive contains a wide range of cycling data across different temperatures, chemistries, and load conditions, reflecting realistic use scenarios. By identifying degradation patterns such as percent of initial capacity and efficiency loss, the proposed algorithm allows QuESt users to better predict how ESS performance changes over time. This integration enhances forecast accuracy, supporting more informed investment decisions and helping reduce long-term energy costs through improved ESS deployment strategies.

Publication Title

ASME International Mechanical Engineering Congress and Exposition Proceedings Imece

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